Generating database cluster health alerts using machine learning
US10373065B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Mar 8, 2013 |
| Grant date | Aug 6, 2019 |
| Priority date | — |
| Expiry date | Jul 16, 2033 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06F11/3452
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A method, system, and computer program product for generating database cluster health alerts using machine learning. A first database cluster known to be operating normally is measured and modeled using machine learning techniques. A second database cluster is measured and compared to the learned model. More specifically, the method collects a first set of empirically-measured variables of a first database cluster, and using the first set of empirically-measured variables a mathematical behavior predictor model is generated. Then, after collecting a second set of empirically-measured variables of a second database cluster over a plurality of second time periods, the mathematical behavior predictor model classifies the observed behavior. The classified behavior might be deemed to be normal behavior, or some form of abnormal behavior. The method forms and report alerts when the classification deemed to be anomalous behavior, or fault behavior. A Bayesian belief network predicts the likelihood of continued anomalous behavior.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.